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  4. Speaker-Independent Microphone Identification in Noisy Conditions
 
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2022
Conference Paper
Titel

Speaker-Independent Microphone Identification in Noisy Conditions

Abstract
This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings.
Author(s)
Giganti, Antonio
Politecnico di Milano
Cuccovillo, Luca
Fraunhofer-Institut für Digitale Medientechnologie IDMT
Bestagini, Paolo
Politecnico di Milano
Aichroth, Patrick
Fraunhofer-Institut für Digitale Medientechnologie IDMT
Tubaro, Stefano
Hauptwerk
30th European Signal Processing Conference, EUSIPCO 2022. Proceedings
Konferenz
European Signal Processing Conference 2022
Thumbnail Image
DOI
10.23919/EUSIPCO55093.2022.9909800
Language
English
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Fraunhofer-Institut für Digitale Medientechnologie IDMT
Tags
  • Audio Forensics

  • Source Attribution

  • Microphone Identifica...

  • Device Fingerprint

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